Overview

Dataset statistics

Number of variables17
Number of observations45215
Missing cells8
Missing cells (%)< 0.1%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory5.9 MiB
Average record size in memory136.0 B

Variable types

Numeric7
Categorical6
Boolean4

Alerts

Dataset has 4 (< 0.1%) duplicate rowsDuplicates
education is highly imbalanced (51.3%)Imbalance
default is highly imbalanced (87.0%)Imbalance
poutcome is highly imbalanced (63.7%)Imbalance
balance is highly skewed (γ1 = 57.21509551)Skewed
previous is highly skewed (γ1 = 41.84300806)Skewed
balance has 3514 (7.8%) zerosZeros
previous has 36957 (81.7%) zerosZeros

Reproduction

Analysis started2024-05-21 18:06:53.869313
Analysis finished2024-05-21 18:07:43.627191
Duration49.76 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct85
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.004711
Minimum18
Maximum776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-05-21T13:07:44.459229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile27
Q133
median39
Q348
95-th percentile59
Maximum776
Range758
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.036647
Coefficient of variation (CV)0.29354304
Kurtosis474.51164
Mean41.004711
Median Absolute Deviation (MAD)7
Skewness10.248147
Sum1854028
Variance144.88088
MonotonicityNot monotonic
2024-05-21T13:07:45.804097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 2084
 
4.6%
31 1995
 
4.4%
33 1972
 
4.4%
34 1930
 
4.3%
35 1894
 
4.2%
36 1806
 
4.0%
30 1757
 
3.9%
37 1696
 
3.8%
39 1486
 
3.3%
38 1466
 
3.2%
Other values (75) 27129
60.0%
ValueCountFrequency (%)
18 12
 
< 0.1%
19 35
 
0.1%
20 50
 
0.1%
21 79
 
0.2%
22 129
 
0.3%
23 201
 
0.4%
24 302
 
0.7%
25 527
1.2%
26 805
1.8%
27 909
2.0%
ValueCountFrequency (%)
776 1
< 0.1%
530 1
< 0.1%
490 1
< 0.1%
466 1
< 0.1%
399 1
< 0.1%
332 1
< 0.1%
311 1
< 0.1%
123 1
< 0.1%
95 2
< 0.1%
94 1
< 0.1%

job
Categorical

Distinct18
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size353.4 KiB
blue-collar
9731 
management
9455 
technician
7599 
admin.
5168 
services
4153 
Other values (13)
9107 

Length

Max length14
Median length13
Mean length9.486077
Min length6

Characters and Unicode

Total characters428894
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowmanagement
2nd rowtechnician
3rd rowentrepreneur
4th rowblue-collar
5th rowunknown

Common Values

ValueCountFrequency (%)
blue-collar 9731
21.5%
management 9455
20.9%
technician 7599
16.8%
admin. 5168
11.4%
services 4153
9.2%
retired 2263
 
5.0%
self-employed 1578
 
3.5%
entrepreneur 1487
 
3.3%
unemployed 1303
 
2.9%
housemaid 1240
 
2.7%
Other values (8) 1236
 
2.7%

Length

2024-05-21T13:07:47.187274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
blue-collar 9731
21.5%
management 9459
20.9%
technician 7599
16.8%
admin 5168
11.4%
services 4154
9.2%
retired 2264
 
5.0%
self-employed 1579
 
3.5%
entrepreneur 1487
 
3.3%
unemployed 1303
 
2.9%
housemaid 1240
 
2.7%
Other values (3) 1229
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 64552
15.1%
n 45362
10.6%
a 42658
9.9%
l 33654
 
7.8%
c 29083
 
6.8%
m 28205
 
6.6%
i 28033
 
6.5%
r 22876
 
5.3%
t 22689
 
5.3%
u 14987
 
3.5%
Other values (22) 96795
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 412391
96.2%
Dash Punctuation 11310
 
2.6%
Other Punctuation 5168
 
1.2%
Uppercase Letter 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64552
15.7%
n 45362
11.0%
a 42658
10.3%
l 33654
8.2%
c 29083
 
7.1%
m 28205
 
6.8%
i 28033
 
6.8%
r 22876
 
5.5%
t 22689
 
5.5%
u 14987
 
3.6%
Other values (12) 80292
19.5%
Uppercase Letter
ValueCountFrequency (%)
M 6
24.0%
A 4
16.0%
N 4
16.0%
E 4
16.0%
G 2
 
8.0%
T 2
 
8.0%
S 2
 
8.0%
R 1
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 11310
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 412416
96.2%
Common 16478
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64552
15.7%
n 45362
11.0%
a 42658
10.3%
l 33654
8.2%
c 29083
 
7.1%
m 28205
 
6.8%
i 28033
 
6.8%
r 22876
 
5.5%
t 22689
 
5.5%
u 14987
 
3.6%
Other values (20) 80317
19.5%
Common
ValueCountFrequency (%)
- 11310
68.6%
. 5168
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 428894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 64552
15.1%
n 45362
10.6%
a 42658
9.9%
l 33654
 
7.8%
c 29083
 
6.8%
m 28205
 
6.6%
i 28033
 
6.5%
r 22876
 
5.3%
t 22689
 
5.3%
u 14987
 
3.5%
Other values (22) 96795
22.6%

marital
Categorical

Distinct6
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size353.4 KiB
married
27215 
single
12787 
divorced
5198 
div.
 
7
Single
 
4

Length

Max length8
Median length7
Mean length6.8316672
Min length4

Characters and Unicode

Total characters308887
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowsingle

Common Values

ValueCountFrequency (%)
married 27215
60.2%
single 12787
28.3%
divorced 5198
 
11.5%
div. 7
 
< 0.1%
Single 4
 
< 0.1%
DIVORCED 3
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2024-05-21T13:07:48.335039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T13:07:49.260442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
married 27215
60.2%
single 12791
28.3%
divorced 5201
 
11.5%
div 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 59628
19.3%
i 45211
14.6%
e 45204
14.6%
d 37618
12.2%
m 27215
8.8%
a 27215
8.8%
n 12791
 
4.1%
g 12791
 
4.1%
l 12791
 
4.1%
s 12787
 
4.1%
Other values (12) 15636
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 308852
> 99.9%
Uppercase Letter 28
 
< 0.1%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 59628
19.3%
i 45211
14.6%
e 45204
14.6%
d 37618
12.2%
m 27215
8.8%
a 27215
8.8%
n 12791
 
4.1%
g 12791
 
4.1%
l 12791
 
4.1%
s 12787
 
4.1%
Other values (3) 15601
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
D 6
21.4%
S 4
14.3%
I 3
10.7%
V 3
10.7%
O 3
10.7%
R 3
10.7%
C 3
10.7%
E 3
10.7%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 308880
> 99.9%
Common 7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 59628
19.3%
i 45211
14.6%
e 45204
14.6%
d 37618
12.2%
m 27215
8.8%
a 27215
8.8%
n 12791
 
4.1%
g 12791
 
4.1%
l 12791
 
4.1%
s 12787
 
4.1%
Other values (11) 15629
 
5.1%
Common
ValueCountFrequency (%)
. 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308887
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 59628
19.3%
i 45211
14.6%
e 45204
14.6%
d 37618
12.2%
m 27215
8.8%
a 27215
8.8%
n 12791
 
4.1%
g 12791
 
4.1%
l 12791
 
4.1%
s 12787
 
4.1%
Other values (12) 15636
 
5.1%

education
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size353.4 KiB
secondary
23197 
tertiary
13302 
primary
6849 
unknown
 
1855
SECONDARY
 
3
Other values (5)
 
8

Length

Max length9
Median length9
Mean length8.3201884
Min length3

Characters and Unicode

Total characters376189
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowtertiary
2nd rowsecondary
3rd rowsecondary
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
secondary 23197
51.3%
tertiary 13302
29.4%
primary 6849
 
15.1%
unknown 1855
 
4.1%
SECONDARY 3
 
< 0.1%
Primary 2
 
< 0.1%
sec. 2
 
< 0.1%
UNK 2
 
< 0.1%
Secondary 1
 
< 0.1%
Tertiary 1
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2024-05-21T13:07:50.136414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T13:07:50.945789image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
secondary 23201
51.3%
tertiary 13303
29.4%
primary 6851
 
15.2%
unknown 1855
 
4.1%
sec 2
 
< 0.1%
unk 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 63506
16.9%
a 43352
11.5%
y 43352
11.5%
e 36503
9.7%
n 28763
7.6%
t 26605
7.1%
o 25053
 
6.7%
c 23200
 
6.2%
s 23199
 
6.2%
d 23198
 
6.2%
Other values (20) 39458
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 376150
> 99.9%
Uppercase Letter 37
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 63506
16.9%
a 43352
11.5%
y 43352
11.5%
e 36503
9.7%
n 28763
7.6%
t 26605
7.1%
o 25053
 
6.7%
c 23200
 
6.2%
s 23199
 
6.2%
d 23198
 
6.2%
Other values (6) 39419
10.5%
Uppercase Letter
ValueCountFrequency (%)
N 5
13.5%
S 4
10.8%
E 3
8.1%
C 3
8.1%
O 3
8.1%
D 3
8.1%
A 3
8.1%
R 3
8.1%
Y 3
8.1%
P 2
 
5.4%
Other values (3) 5
13.5%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 376187
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 63506
16.9%
a 43352
11.5%
y 43352
11.5%
e 36503
9.7%
n 28763
7.6%
t 26605
7.1%
o 25053
 
6.7%
c 23200
 
6.2%
s 23199
 
6.2%
d 23198
 
6.2%
Other values (19) 39456
10.5%
Common
ValueCountFrequency (%)
. 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 376189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 63506
16.9%
a 43352
11.5%
y 43352
11.5%
e 36503
9.7%
n 28763
7.6%
t 26605
7.1%
o 25053
 
6.7%
c 23200
 
6.2%
s 23199
 
6.2%
d 23198
 
6.2%
Other values (20) 39458
10.5%

default
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
44399 
True
 
816
ValueCountFrequency (%)
False 44399
98.2%
True 816
 
1.8%
2024-05-21T13:07:51.658907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

balance
Real number (ℝ)

SKEWED  ZEROS 

Distinct7168
Distinct (%)15.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1374.1599
Minimum-8019
Maximum527532
Zeros3514
Zeros (%)7.8%
Negative3767
Negative (%)8.3%
Memory size353.4 KiB
2024-05-21T13:07:52.374835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8019
5-th percentile-172
Q172
median448
Q31428
95-th percentile5769
Maximum527532
Range535551
Interquartile range (IQR)1356

Descriptive statistics

Standard deviation3924.2555
Coefficient of variation (CV)2.8557489
Kurtosis7197.9493
Mean1374.1599
Median Absolute Deviation (MAD)448
Skewness57.215096
Sum62129890
Variance15399781
MonotonicityNot monotonic
2024-05-21T13:07:53.316362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3514
 
7.8%
1 195
 
0.4%
2 156
 
0.3%
4 139
 
0.3%
3 134
 
0.3%
5 113
 
0.2%
6 88
 
0.2%
8 81
 
0.2%
23 75
 
0.2%
10 69
 
0.2%
Other values (7158) 40649
89.9%
ValueCountFrequency (%)
-8019 1
< 0.1%
-6847 1
< 0.1%
-4057 1
< 0.1%
-3372 1
< 0.1%
-3313 1
< 0.1%
-3058 1
< 0.1%
-2827 1
< 0.1%
-2712 1
< 0.1%
-2604 1
< 0.1%
-2282 1
< 0.1%
ValueCountFrequency (%)
527532 1
< 0.1%
102127 1
< 0.1%
98417 1
< 0.1%
81204 2
< 0.1%
71188 1
< 0.1%
66721 1
< 0.1%
66653 1
< 0.1%
64343 1
< 0.1%
59649 1
< 0.1%
58932 1
< 0.1%

housing
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
True
25132 
False
20083 
ValueCountFrequency (%)
True 25132
55.6%
False 20083
44.4%
2024-05-21T13:07:54.022131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

loan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
37969 
True
7246 
ValueCountFrequency (%)
False 37969
84.0%
True 7246
 
16.0%
2024-05-21T13:07:54.558082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

contact
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.4 KiB
cellular
29285 
unknown
13021 
telephone
 
2903
phone
 
3
mobile
 
3

Length

Max length9
Median length8
Mean length7.775893
Min length5

Characters and Unicode

Total characters351587
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13021
28.8%
telephone 2903
 
6.4%
phone 3
 
< 0.1%
mobile 3
 
< 0.1%

Length

2024-05-21T13:07:55.346659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T13:07:56.225676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
cellular 29285
64.8%
unknown 13021
28.8%
telephone 2903
 
6.4%
phone 3
 
< 0.1%
mobile 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 90761
25.8%
u 42306
12.0%
n 41969
11.9%
e 38000
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15930
 
4.5%
k 13021
 
3.7%
w 13021
 
3.7%
Other values (6) 8724
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 351587
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 90761
25.8%
u 42306
12.0%
n 41969
11.9%
e 38000
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15930
 
4.5%
k 13021
 
3.7%
w 13021
 
3.7%
Other values (6) 8724
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 351587
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 90761
25.8%
u 42306
12.0%
n 41969
11.9%
e 38000
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15930
 
4.5%
k 13021
 
3.7%
w 13021
 
3.7%
Other values (6) 8724
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 90761
25.8%
u 42306
12.0%
n 41969
11.9%
e 38000
10.8%
c 29285
 
8.3%
a 29285
 
8.3%
r 29285
 
8.3%
o 15930
 
4.5%
k 13021
 
3.7%
w 13021
 
3.7%
Other values (6) 8724
 
2.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.805839
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-05-21T13:07:56.944228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median16
Q321
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.322473
Coefficient of variation (CV)0.52654422
Kurtosis-1.059853
Mean15.805839
Median Absolute Deviation (MAD)7
Skewness0.093156482
Sum714661
Variance69.263557
MonotonicityNot monotonic
2024-05-21T13:07:57.757768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 2752
 
6.1%
18 2308
 
5.1%
21 2026
 
4.5%
17 1939
 
4.3%
6 1932
 
4.3%
5 1910
 
4.2%
14 1848
 
4.1%
8 1843
 
4.1%
28 1830
 
4.0%
7 1817
 
4.0%
Other values (21) 25010
55.3%
ValueCountFrequency (%)
1 322
 
0.7%
2 1294
2.9%
3 1079
2.4%
4 1445
3.2%
5 1910
4.2%
6 1932
4.3%
7 1817
4.0%
8 1843
4.1%
9 1561
3.5%
10 524
 
1.2%
ValueCountFrequency (%)
31 643
 
1.4%
30 1566
3.5%
29 1745
3.9%
28 1830
4.0%
27 1121
2.5%
26 1035
2.3%
25 840
1.9%
24 447
 
1.0%
23 939
2.1%
22 905
2.0%

month
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.4 KiB
may
13768 
jul
6895 
aug
6247 
jun
5342 
nov
3971 
Other values (7)
8992 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters135645
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13768
30.5%
jul 6895
15.2%
aug 6247
13.8%
jun 5342
 
11.8%
nov 3971
 
8.8%
apr 2932
 
6.5%
feb 2649
 
5.9%
jan 1403
 
3.1%
oct 738
 
1.6%
sep 579
 
1.3%
Other values (2) 691
 
1.5%

Length

2024-05-21T13:07:58.543486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
may 13768
30.5%
jul 6895
15.2%
aug 6247
13.8%
jun 5342
 
11.8%
nov 3971
 
8.8%
apr 2932
 
6.5%
feb 2649
 
5.9%
jan 1403
 
3.1%
oct 738
 
1.6%
sep 579
 
1.3%
Other values (2) 691
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 24827
18.3%
u 18484
13.6%
m 14245
10.5%
y 13768
10.2%
j 13640
10.1%
n 10716
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4709
 
3.5%
v 3971
 
2.9%
Other values (9) 18143
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135645
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24827
18.3%
u 18484
13.6%
m 14245
10.5%
y 13768
10.2%
j 13640
10.1%
n 10716
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4709
 
3.5%
v 3971
 
2.9%
Other values (9) 18143
13.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 135645
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 24827
18.3%
u 18484
13.6%
m 14245
10.5%
y 13768
10.2%
j 13640
10.1%
n 10716
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4709
 
3.5%
v 3971
 
2.9%
Other values (9) 18143
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 24827
18.3%
u 18484
13.6%
m 14245
10.5%
y 13768
10.2%
j 13640
10.1%
n 10716
7.9%
l 6895
 
5.1%
g 6247
 
4.6%
o 4709
 
3.5%
v 3971
 
2.9%
Other values (9) 18143
13.4%

duration
Real number (ℝ)

Distinct1575
Distinct (%)3.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean258.07436
Minimum-1389
Maximum4918
Zeros3
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size353.4 KiB
2024-05-21T13:07:59.263590image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1389
5-th percentile35
Q1103
median180
Q3319
95-th percentile750.35
Maximum4918
Range6307
Interquartile range (IQR)216

Descriptive statistics

Standard deviation257.60517
Coefficient of variation (CV)0.99818199
Kurtosis18.161989
Mean258.07436
Median Absolute Deviation (MAD)93
Skewness3.134025
Sum11668574
Variance66360.426
MonotonicityNot monotonic
2024-05-21T13:08:00.207325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 188
 
0.4%
90 184
 
0.4%
89 177
 
0.4%
114 175
 
0.4%
104 175
 
0.4%
122 175
 
0.4%
136 174
 
0.4%
112 174
 
0.4%
139 174
 
0.4%
121 173
 
0.4%
Other values (1565) 43445
96.1%
ValueCountFrequency (%)
-1389 1
 
< 0.1%
-517 1
 
< 0.1%
0 3
 
< 0.1%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 15
 
< 0.1%
5 35
0.1%
6 45
0.1%
7 73
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
3881 1
< 0.1%
3785 1
< 0.1%
3422 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%
3253 1
< 0.1%
3183 1
< 0.1%
3102 1
< 0.1%

campaign
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7637289
Minimum1
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-05-21T13:08:01.094425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum63
Range62
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0979102
Coefficient of variation (CV)1.1209168
Kurtosis39.252662
Mean2.7637289
Median Absolute Deviation (MAD)1
Skewness4.8988412
Sum124962
Variance9.5970477
MonotonicityNot monotonic
2024-05-21T13:08:02.423771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 17546
38.8%
2 12507
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
Other values (38) 1196
 
2.6%
ValueCountFrequency (%)
1 17546
38.8%
2 12507
27.7%
3 5521
 
12.2%
4 3522
 
7.8%
5 1764
 
3.9%
6 1291
 
2.9%
7 735
 
1.6%
8 540
 
1.2%
9 327
 
0.7%
10 266
 
0.6%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
55 1
 
< 0.1%
51 1
 
< 0.1%
50 2
< 0.1%
46 1
 
< 0.1%
44 1
 
< 0.1%
43 3
< 0.1%
41 2
< 0.1%
39 1
 
< 0.1%

pdays
Real number (ℝ)

Distinct559
Distinct (%)1.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.192485
Minimum-1
Maximum871
Zeros0
Zeros (%)0.0%
Negative36957
Negative (%)81.7%
Memory size353.4 KiB
2024-05-21T13:08:03.458598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile317
Maximum871
Range872
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.12062
Coefficient of variation (CV)2.4910284
Kurtosis6.9373411
Mean40.192485
Median Absolute Deviation (MAD)0
Skewness2.6159947
Sum1817263
Variance10024.139
MonotonicityNot monotonic
2024-05-21T13:08:04.478040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 36957
81.7%
182 167
 
0.4%
92 147
 
0.3%
91 126
 
0.3%
183 126
 
0.3%
181 117
 
0.3%
370 99
 
0.2%
184 85
 
0.2%
364 77
 
0.2%
95 74
 
0.2%
Other values (549) 7239
 
16.0%
ValueCountFrequency (%)
-1 36957
81.7%
1 15
 
< 0.1%
2 37
 
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 11
 
< 0.1%
6 10
 
< 0.1%
7 7
 
< 0.1%
8 25
 
0.1%
9 12
 
< 0.1%
ValueCountFrequency (%)
871 1
< 0.1%
854 1
< 0.1%
850 1
< 0.1%
842 1
< 0.1%
838 1
< 0.1%
831 1
< 0.1%
828 1
< 0.1%
826 1
< 0.1%
808 1
< 0.1%
805 1
< 0.1%

previous
Real number (ℝ)

SKEWED  ZEROS 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58038262
Minimum0
Maximum275
Zeros36957
Zeros (%)81.7%
Negative0
Negative (%)0.0%
Memory size353.4 KiB
2024-05-21T13:08:05.360353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum275
Range275
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3034378
Coefficient of variation (CV)3.9688263
Kurtosis4506.4832
Mean0.58038262
Median Absolute Deviation (MAD)0
Skewness41.843008
Sum26242
Variance5.3058256
MonotonicityNot monotonic
2024-05-21T13:08:06.241999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 36957
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 460
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
Other values (31) 361
 
0.8%
ValueCountFrequency (%)
0 36957
81.7%
1 2772
 
6.1%
2 2106
 
4.7%
3 1142
 
2.5%
4 714
 
1.6%
5 460
 
1.0%
6 277
 
0.6%
7 205
 
0.5%
8 129
 
0.3%
9 92
 
0.2%
ValueCountFrequency (%)
275 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
41 1
< 0.1%
40 1
< 0.1%
38 2
< 0.1%
37 2
< 0.1%
35 1
< 0.1%
32 1
< 0.1%

poutcome
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size353.4 KiB
unknown
36958 
failure
4902 
other
 
1840
success
 
1509
UNK
 
4

Length

Max length7
Median length7
Mean length6.9182572
Min length3

Characters and Unicode

Total characters312809
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 36958
81.7%
failure 4902
 
10.8%
other 1840
 
4.1%
success 1509
 
3.3%
UNK 4
 
< 0.1%
Success 2
 
< 0.1%

Length

2024-05-21T13:08:07.265847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-21T13:08:08.042306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unknown 36958
81.7%
failure 4902
 
10.8%
other 1840
 
4.1%
success 1511
 
3.3%
unk 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 110874
35.4%
u 43371
 
13.9%
o 38798
 
12.4%
k 36958
 
11.8%
w 36958
 
11.8%
e 8253
 
2.6%
r 6742
 
2.2%
i 4902
 
1.6%
l 4902
 
1.6%
a 4902
 
1.6%
Other values (9) 16149
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 312795
> 99.9%
Uppercase Letter 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 110874
35.4%
u 43371
 
13.9%
o 38798
 
12.4%
k 36958
 
11.8%
w 36958
 
11.8%
e 8253
 
2.6%
r 6742
 
2.2%
i 4902
 
1.6%
l 4902
 
1.6%
a 4902
 
1.6%
Other values (5) 16135
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
U 4
28.6%
N 4
28.6%
K 4
28.6%
S 2
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 312809
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 110874
35.4%
u 43371
 
13.9%
o 38798
 
12.4%
k 36958
 
11.8%
w 36958
 
11.8%
e 8253
 
2.6%
r 6742
 
2.2%
i 4902
 
1.6%
l 4902
 
1.6%
a 4902
 
1.6%
Other values (9) 16149
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 110874
35.4%
u 43371
 
13.9%
o 38798
 
12.4%
k 36958
 
11.8%
w 36958
 
11.8%
e 8253
 
2.6%
r 6742
 
2.2%
i 4902
 
1.6%
l 4902
 
1.6%
a 4902
 
1.6%
Other values (9) 16149
 
5.2%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.3 KiB
False
39925 
True
5290 
ValueCountFrequency (%)
False 39925
88.3%
True 5290
 
11.7%
2024-05-21T13:08:08.660961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Interactions

2024-05-21T13:07:33.652418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:06:57.386525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:09.356078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:14.047707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:18.972686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:23.807038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:28.514123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:34.607957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:00.471086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:10.112946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:14.756122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:19.772820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:24.558625image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:29.223732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:35.408969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:03.770617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:10.729932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:15.403534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:20.456219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:25.173298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:29.892552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:36.138128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:06.785261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:11.378229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:16.339233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:21.106335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:25.857064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:30.540430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:36.924457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:07.437712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:12.002536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:17.007995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:21.773207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:26.506782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:31.224873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:37.682407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:08.088897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:12.653258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:17.672898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:22.422908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:27.187104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:31.991395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:38.381584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:08.697987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:13.333400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:18.306101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:23.088829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:27.823190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-21T13:07:32.834960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-21T13:07:39.609509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-21T13:07:41.716268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-21T13:07:43.025698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
058managementmarriedtertiaryno2143.0yesnounknown5may261.01-1.00unknownno
144techniciansinglesecondaryno29.0yesnounknown5may151.01-1.00unknownno
233entrepreneurmarriedsecondaryno2.0yesyesunknown5may76.01-1.00unknownno
347blue-collarmarriedunknownno1506.0yesnounknown5may92.01-1.00unknownno
433unknownsingleunknownno1.0nonounknown5may198.01-1.00unknownno
535managementmarriedtertiaryno231.0yesnounknown5may139.01-1.00unknownno
628Managementsingletertiaryno447.0yesyesunknown5may217.01-1.00unknownno
742entrepreneurdiv.tertiaryyes2.0yesnounknown5may380.01-1.00unknownno
858retiredmarriedprimaryno121.0yesnounknown5may50.01-1.00unknownno
943techniciansinglesecondaryno593.0yesNounknown5may55.01-1.00unknownno
agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey
4520553managementmarriedtertiaryno583.0nonocellular17nov226.01184.04successyes
4520634admin.singlesecondaryno557.0nonocellular17nov224.01-1.00unknownyes
4520723studentsingletertiaryno113.0nonocellular17nov266.01-1.00unknownyes
4520873retiredmarriedsecondaryno2850.0nonocellular17nov300.0140.08failureyes
4520925techniciansinglesecondaryno505.0noyescellular17nov386.02-1.00unknownyes
4521051technicianmarriedtertiaryno825.0nonocellular17nov977.03-1.00unknownyes
4521171retireddivorcedprimaryno1729.0nonocellular17nov456.02-1.00unknownyes
4521272retiredmarriedsecondaryno5715.0nonocellular17nov1127.05184.03successyes
4521357blue-collarmarriedsecondaryno668.0nonotelephone17nov508.04-1.00unknownno
4521437entrepreneurmarriedsecondaryno2971.0nonocellular17nov361.02188.011otherno

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaultbalancehousingloancontactdaymonthdurationcampaignpdayspreviouspoutcomey# duplicates
029techniciansingletertiaryno18254.0nonocellular11may279.02-1.00unknownno2
143blue-collarmarriedsecondaryyes-7.0nonounknown8may70.01-1.00unknownno2
252techniciandivorcedsecondaryno1005.0yesnocellular2jun195.01-1.00unknownyes2
359managementmarriedtertiaryno138.0yesyescellular16nov162.02187.05failureno2